Towards sustainable agriculture: A lightweight hybrid model and cloud-based collection of datasets for efficient leaf disease detection

Agricultural sustainability is a crucial component of the global economy and faces several challenges, including plant diseases. However, the application of deep learning models in unmanned aerial vehicles (UAVs) to detect plant diseases is hindered by their computational complexity and the lack of public datasets and information in this field. In this paper, we have two objectives. Firstly, we present a cloud-based collection by compiling and analyzing 38 available public datasets, which simplifies the work of researchers by reducing the time spent searching for suitable datasets. Secondly, we propose a lightweight model named Tiny-LeViT based on the transformer architecture for efficient leaf disease classification in edge network contexts. Our experiments on five popular datasets show that the proposed model outperforms its competitors, achieving at least 9% higher frame rate while retaining comparable F1-scores with an average of 97.25% on both RTX workstation and edge devices.& COPY; 2023 Elsevier B.V. All rights reserved.